Местные учебные курсы, посвященные искусственному интеллекту (AI), демонстрируют практическую практику практического применения AI решений для решения реальных проблем Обучение AI доступно как «живое обучение на месте» или «дистанционное живое обучение» На месте живое обучение может проводиться локально в помещениях клиента в Russia или в корпоративных учебных центрах NobleProg Russia , Дистанционное обучение в реальном времени осуществляется с помощью интерактивного удаленного рабочего стола NobleProg Ваш местный провайдер обучения.
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Отзывы
диапазон материала Maciej Jonczy
Maciej Jonczyk
Курсы: From Data to Decision with Big Data and Predictive Analytics
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систематизация знаний в области ML
Orange Polska
Курсы: From Data to Decision with Big Data and Predictive Analytics
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Я действительно оценил кристально чистые ответы Криса на наши вопросы. Léo Dubus
Léo Dubus
Курсы: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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Знающий тренер
Sridhar Voorakkara
Курсы: Neural Networks Fundamentals using TensorFlow as Example
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Я был поражен стандартом этого класса, я бы сказал, что это университетский стандарт. ,
David Relihan
Курсы: Neural Networks Fundamentals using TensorFlow as Example
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Очень хороший круглый обзор. Хороший фон в том, почему Tensorflow работает так же, как и он
Kieran Conboy
Курсы: Neural Networks Fundamentals using TensorFlow as Example
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Мне нравились возможности задавать вопросы и более подробно объяснять теорию. ,
Sharon Ruane
Курсы: Neural Networks Fundamentals using TensorFlow as Example
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Мы получили гораздо больше информации в этом вопросе. Некоторые приятные дискуссии были сделаны с некоторыми реальными темами в нашей компании
Sebastiaan Holman
Курсы: Machine Learning and Deep Learning
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Тренинг обеспечил правильный фундамент, который позволяет нам дальше расширяться, демонстрируя, как теория и практика идут рука об руку. На самом деле меня больше заинтересовал предмет, чем раньше
Jean-Paul van Tillo
Курсы: Machine Learning and Deep Learning
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Охват и глубина тем
Anirban Basu
Курсы: Machine Learning and Deep Learning
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Тренер очень легко объяснил сложные и продвинутые темы.
Leszek K
Курсы: Artificial Intelligence Overview
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новые идеи глубокого машинного обучения
Josip Arneric
Курсы: Neural Network in R
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Мы получили некоторые знания о NN в целом, и для меня наиболее интересными были новые типы NN, которые сегодня популярны
Tea Poklepovic
Курсы: Neural Network in R
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Графики в R :)))
Faculty of Economics and Business Zagreb
Курсы: Neural Network in R
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Глубокое знание тренера по теме
Sebastian Görg
Курсы: Introduction to Deep Learning
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Очень обновленный подход или api (tensorflow, kera, tflearn) для машинного обучения
Paul Lee
Курсы: TensorFlow for Image Recognition
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Очень гибкий
Frank Ueltzhöffer
Курсы: Artificial Neural Networks, Machine Learning and Deep Thinking
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гибкость
Werner Philipp
Курсы: Artificial Neural Networks, Machine Learning and Deep Thinking
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Учитывая перспективы технологии: какие технологии / процесс могут стать более важными в будущем; см., для чего технология может быть использована
Commerzbank AG
Курсы: Neural Networks Fundamentals using TensorFlow as Example
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Выбор темы. Стиль обучения. Ориентация на практику
Commerzbank AG
Курсы: Neural Networks Fundamentals using TensorFlow as Example
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Все как это 李 蒙 -
蒙 李
Курсы: Machine Learning Fundamentals with Python
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способ проведения и пример, данный тренером ORANGE POLSKA S.A.
ORANGE POLSKA S.A.
Курсы: Machine Learning and Deep Learning
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Возможность самостоятельно обсудить предлагаемые вопросы ORANGE POLSKA S.A.
ORANGE POLSKA S.A.
Курсы: Machine Learning and Deep Learning
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Общение с преподавателями 张 文欣 - Accenture
文欣 张
Курсы: Artificial Neural Networks, Machine Learning, Deep Thinking
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Нравится
lisa xie
Курсы: Artificial Neural Networks, Machine Learning, Deep Thinking
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Непосредственный охват вопросов машинного обучения, особенно нейронных сетей. Демистифицировал много темы
Sacha Nandlall
Курсы: Python for Advanced Machine Learning
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Это один из лучших handsons с курсами программирования упражнений, которые я когда-либо делал
Laura Kahn
Курсы: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Это одно из лучших онлайн-тренингов, которые я когда-либо занимал в своей 13-летней карьере. Продолжайте в том же духе
Курсы: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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много упражнений, которые я могу непосредственно использовать в своей работе.
Alior Bank S.A.
Курсы: Sieci Neuronowe w R
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Примеры реальных данных.
Alior Bank S.A.
Курсы: Sieci Neuronowe w R
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neuralnet, pROC в цикле.
Alior Bank S.A.
Курсы: Sieci Neuronowe w R
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Стиль обучения Ричарда удержал его интересным образом, примеры реальных миров помогли водить концепты дома ..
Jamie Martin-Royle - NBrown Group
Курсы: From Data to Decision with Big Data and Predictive Analytics
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Содержание, поскольку я нашел это очень интересным и думаю, что это помогло бы мне в моем последнем году в Университете.
Krishan Mistry - NBrown Group
Курсы: From Data to Decision with Big Data and Predictive Analytics
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Мне искренне нравились упражнения
L M ERICSSON LIMITED
Курсы: Machine Learning
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Это одно из лучших онлайн-тренингов, которые я когда-либо занимал в своей 13-летней карьере. Продолжайте в том же духе
Курсы: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Artificial Intelligence (AI) Подкатегории
Artificial Intelligence (AI) Содержание курса
Название курса
Продолжительность
Обзор
Название курса
Продолжительность
Обзор
7 часов
Обзор
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
21 часов
Обзор
This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.
28 часов
Обзор
This four day course is aimed at teaching how genetic algorithms work; it also covers how to select model parameters of a genetic algorithm; there are many applications for genetic algorithms in this course and optimization problems are tackled with the genetic algorithms.
7 часов
Обзор
This is a classroom based training session in a presentation and Q&A format
14 часов
Обзор
This instructor-led, live training in Russia (online or onsite) is aimed at technical persons who wish to set up or extend an RPA system with more intelligent capabilities.
By the end of this training, participants will be able to:
- Install and configure UiPath IPA.
- Enable robots to manage other robots.
- Apply computer vision to locate screen objects with accuracy.
- Enable robots that can detect language patterns and carry out sentiment analysis on unstructured content.
By the end of this training, participants will be able to:
- Install and configure UiPath IPA.
- Enable robots to manage other robots.
- Apply computer vision to locate screen objects with accuracy.
- Enable robots that can detect language patterns and carry out sentiment analysis on unstructured content.
14 часов
Обзор
This instructor-led, live training in Russia (online or onsite) is aimed at software testers who wish to have an AI driven software testing environment.
By the end of this training, participants will be able to:
- Automate unit test generation and parameterization with AI.
- Apply machine learning learning in a real world use-case.
- Automate the generation and maintenance of API tests with AI.
- Use machine learning methods to self-heal the execution of Selenium tests.
By the end of this training, participants will be able to:
- Automate unit test generation and parameterization with AI.
- Apply machine learning learning in a real world use-case.
- Automate the generation and maintenance of API tests with AI.
- Use machine learning methods to self-heal the execution of Selenium tests.
35 часов
Обзор
This is a 5 day introduction to Data Science and Artificial Intelligence (AI).
The course is delivered with examples and exercises using Python
The course is delivered with examples and exercises using Python
7 часов
Обзор
This instructor-led, live training in Russia (online or onsite) is aimed at marketers who wish to use AI to improve improve digital marketing strategies through valuable customer insights.
By the end of this training, participants will be able to:
- Leverage AI software to improve the way brands connect to users.
- Use chatbots to optimize the user-experience.
- Increase productivity and revenue through the automation of tasks.
By the end of this training, participants will be able to:
- Leverage AI software to improve the way brands connect to users.
- Use chatbots to optimize the user-experience.
- Increase productivity and revenue through the automation of tasks.
14 часов
Обзор
This instructor-led, live training in Russia (online or onsite) is aimed at data scientists who wish to use IBM Cloud Pak to prepare data for use in AI solutions.
By the end of this training, participants will be able to:
- Install and configure Cloud Pak for Data.
- Unify the collection, organization and analysis of data.
- Integrate Cloud Pak for Data with a variety of services to solve common business problems.
- Implement workflows for collaborating with team members on the development of an AI solution.
By the end of this training, participants will be able to:
- Install and configure Cloud Pak for Data.
- Unify the collection, organization and analysis of data.
- Integrate Cloud Pak for Data with a variety of services to solve common business problems.
- Implement workflows for collaborating with team members on the development of an AI solution.
21 часов
Обзор
This instructor-led, live training in Russia (online or onsite) is aimed at engineers who wish to program and create robots through basic AI methods.
By the end of this training, participants will be able to:
- Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
By the end of this training, participants will be able to:
- Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
7 часов
Обзор
This instructor-led, live training in Russia (online or onsite) is aimed at managers and business leaders who wish to learn about the fundamentals of artificial intelligence and manage AI projects for their organization.
By the end of this training, participants will be able to understand AI at a technical level and strategize using their organization’s data and resources to successfully manage AI projects.
By the end of this training, participants will be able to understand AI at a technical level and strategize using their organization’s data and resources to successfully manage AI projects.
80 часов
Обзор
In this instructor-led, live training in Russia (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Test and troubleshoot a robot in realistic scenarios.
The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Test and troubleshoot a robot in realistic scenarios.
120 часов
Обзор
In this instructor-led, live training in Russia (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Extend a robot's ability to perform complex tasks through Deep Learning.
- Test and troubleshoot a robot in realistic scenarios.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Extend a robot's ability to perform complex tasks through Deep Learning.
- Test and troubleshoot a robot in realistic scenarios.
7 часов
Обзор
The training is aimed at people who want to learn the basics of neural networks and their applications.
14 часов
Обзор
This course is an introduction to applying neural networks in real world problems using R-project software.
14 часов
Обзор
This training course is for people that would like to apply Machine Learning in practical applications.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
21 часов
Обзор
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
21 часов
Обзор
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
35 часов
Обзор
This course is created for people who have no previous experience in probability and statistics.
14 часов
Обзор
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
28 часов
Обзор
This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
21 часов
Обзор
This instructor-led, live course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.
The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.
21 часов
Обзор
Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
7 часов
Обзор
In this instructor-led, live training in Russia, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
21 часов
Обзор
Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.
In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.
By the end of this training, participants will be able to:
- Access CNTK as a library from within a Python, C#, or C++ program
- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
- Use the CNTK model evaluation functionality from a Java program
- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
- Scale computation capacity on CPUs, GPUs and multiple machines
- Access massive datasets using existing programming languages and algorithms
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.
By the end of this training, participants will be able to:
- Access CNTK as a library from within a Python, C#, or C++ program
- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
- Use the CNTK model evaluation functionality from a Java program
- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
- Scale computation capacity on CPUs, GPUs and multiple machines
- Access massive datasets using existing programming languages and algorithms
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
21 часов
Обзор
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7 часов
Обзор
Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.
In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 часов
Обзор
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.
By the end of this training, participants will be able to:
- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.
By the end of this training, participants will be able to:
- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 часов
Обзор
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.
By the end of this training, participants will be able to:
- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.
By the end of this training, participants will be able to:
- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 часов
Обзор
In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.
By the end of this training, participants will be able to:
- Build a deep learning model
- Automate data labeling
- Work with models from Caffe and TensorFlow-Keras
- Train data using multiple GPUs, the cloud, or clusters
Audience
- Developers
- Engineers
- Domain experts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
By the end of this training, participants will be able to:
- Build a deep learning model
- Automate data labeling
- Work with models from Caffe and TensorFlow-Keras
- Train data using multiple GPUs, the cloud, or clusters
Audience
- Developers
- Engineers
- Domain experts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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